Building Intelligence in the Automated Traffic Signal Performance Measures with Advanced Data Analytics

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dc.contributor.author Huang, Tingting
dc.contributor.author Poddar, Subhadipto
dc.contributor.author Aguilar, Cristopher
dc.contributor.author Sharma, Anuj
dc.contributor.author Sharma, Anuj
dc.contributor.author Smaglik, Edward
dc.contributor.author Kothuri, Sirisha
dc.contributor.author Koonce, Peter
dc.contributor.department Civil, Construction and Environmental Engineering
dc.contributor.department Institute for Transportation
dc.date 2019-11-27T15:39:24.000
dc.date.accessioned 2020-06-30T01:11:42Z
dc.date.available 2020-06-30T01:11:42Z
dc.date.copyright Mon Jan 01 00:00:00 UTC 2018
dc.date.embargo 2018-04-02
dc.date.issued 2018-01-01
dc.description.abstract <p>Automated traffic signal performance measures (ATSPMs) are an effort to equip traffic signal controllers with high-resolution data-logging capabilities and utilize this data to generate performance measures. These measures allow practitioners to improve operations as well as to maintain and operate their systems in a safe and efficient manner. Although these measures have changed the way that operators manage their systems, several shortcomings of the tool, identified by talking with signal operators, are a lack of data quality control and the extent of resources required to properly use the tool for system-wide management. To address these shortcomings, intelligent traffic signal performance measurements (ITSPMs) are presented in this paper, using the concepts of machine learning, traffic flow theory, and data visualization to reduce the operator resources needed for overseeing data-driven traffic signal management systems. In applying these concepts, ITSPMs provide graphical tools to identify and remove logging errors and data from bad sensors, intelligently determine trends in demand, and address the question of whether or not coordination may be needed at an intersection. The focus of ATSPMs and ITSPMs on performance measures for multimodal users is identified as a pressing need for future research.</p>
dc.description.comments <p>This is a manuscript of a proceeding published as Huang, Tingting, Subhadipto Poddar, Cristopher Aguilar, Anuj Sharma, Edward Smaglik, Sirisha Kothuri, and Peter Koonce. "Building Intelligence in the Automated Traffic Signal Performance Measures with Advanced Data Analytics." No. 18-05800. 2018. Transportation Research Board 97th Annual Meeting, Washington, DC, January 7-11, 2018. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/ccee_conf/80/
dc.identifier.articleid 1080
dc.identifier.contextkey 11888340
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath ccee_conf/80
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/13707
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/ccee_conf/80/2018_Sharma_BuildingIntelligence.pdf|||Sat Jan 15 02:04:52 UTC 2022
dc.subject.disciplines Civil Engineering
dc.subject.disciplines Computer-Aided Engineering and Design
dc.subject.disciplines Transportation Engineering
dc.title Building Intelligence in the Automated Traffic Signal Performance Measures with Advanced Data Analytics
dc.type article
dc.type.genre conference
dspace.entity.type Publication
relation.isAuthorOfPublication 717eae32-77e8-420a-b66c-a44c60495a6b
relation.isOrgUnitOfPublication 933e9c94-323c-4da9-9e8e-861692825f91
relation.isOrgUnitOfPublication 0cffd73a-b46d-4816-85f3-0f6ab7d2beb8
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